102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
import torch
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from thop import profile
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from networks.subter_LeNet import SubTer_LeNet, SubTer_LeNet_Autoencoder
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from networks.subter_LeNet_rf import SubTer_Efficient_AE, SubTer_EfficientEncoder
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# Configuration
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LATENT_DIMS = [32, 64, 128, 256, 512, 768, 1024]
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BATCH_SIZE = 1
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INPUT_SHAPE = (BATCH_SIZE, 1, 32, 2048)
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def count_parameters(model, input_shape):
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"""Count MACs and parameters for a model."""
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model.eval()
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with torch.no_grad():
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input_tensor = torch.randn(input_shape)
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macs, params = profile(model, inputs=(input_tensor,))
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return {"MACs": macs, "Parameters": params}
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def format_number(num: float) -> str:
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"""Format large numbers with K, M, B, T suffixes."""
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for unit in ["", "K", "M", "B", "T"]:
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if abs(num) < 1000.0 or unit == "T":
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return f"{num:3.2f}{unit}"
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num /= 1000.0
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def main():
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# Collect results per latent dimension
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results = {} # dim -> dict of 8 values
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for dim in LATENT_DIMS:
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# Instantiate models for this latent dim
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lenet_enc = SubTer_LeNet(rep_dim=dim)
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eff_enc = SubTer_EfficientEncoder(rep_dim=dim)
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lenet_ae = SubTer_LeNet_Autoencoder(rep_dim=dim)
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eff_ae = SubTer_Efficient_AE(rep_dim=dim)
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# Profile each
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lenet_enc_stats = count_parameters(lenet_enc, INPUT_SHAPE)
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eff_enc_stats = count_parameters(eff_enc, INPUT_SHAPE)
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lenet_ae_stats = count_parameters(lenet_ae, INPUT_SHAPE)
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eff_ae_stats = count_parameters(eff_ae, INPUT_SHAPE)
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results[dim] = {
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"lenet_enc_params": format_number(lenet_enc_stats["Parameters"]),
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"lenet_enc_macs": format_number(lenet_enc_stats["MACs"]),
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"eff_enc_params": format_number(eff_enc_stats["Parameters"]),
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"eff_enc_macs": format_number(eff_enc_stats["MACs"]),
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"lenet_ae_params": format_number(lenet_ae_stats["Parameters"]),
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"lenet_ae_macs": format_number(lenet_ae_stats["MACs"]),
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"eff_ae_params": format_number(eff_ae_stats["Parameters"]),
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"eff_ae_macs": format_number(eff_ae_stats["MACs"]),
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}
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# Build LaTeX table with tabularx
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header = (
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"\\begin{table}[!ht]\n"
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"\\centering\n"
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"\\renewcommand{\\arraystretch}{1.15}\n"
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"\\begin{tabularx}{\\linewidth}{lXXXXXXXX}\n"
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"\\hline\n"
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" & \\multicolumn{4}{c}{\\textbf{Encoders}} & "
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"\\multicolumn{4}{c}{\\textbf{Autoencoders}} \\\\\n"
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"\\cline{2-9}\n"
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"\\textbf{Latent $z$} & "
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"\\textbf{LeNet Params} & \\textbf{LeNet MACs} & "
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"\\textbf{Eff. Params} & \\textbf{Eff. MACs} & "
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"\\textbf{LeNet Params} & \\textbf{LeNet MACs} & "
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"\\textbf{Eff. Params} & \\textbf{Eff. MACs} \\\\\n"
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"\\hline\n"
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)
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rows = []
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for dim in LATENT_DIMS:
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r = results[dim]
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row = (
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f"{dim} & "
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f"{r['lenet_enc_params']} & {r['lenet_enc_macs']} & "
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f"{r['eff_enc_params']} & {r['eff_enc_macs']} & "
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f"{r['lenet_ae_params']} & {r['lenet_ae_macs']} & "
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f"{r['eff_ae_params']} & {r['eff_ae_macs']} \\\\"
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)
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rows.append(row)
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footer = (
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"\\hline\n"
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"\\end{tabularx}\n"
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"\\caption{Parameter and MAC counts for SubTer variants across latent dimensionalities.}\n"
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"\\label{tab:subter_counts}\n"
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"\\end{table}\n"
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)
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latex_table = header + "\n".join(rows) + "\n" + footer
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print(latex_table)
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if __name__ == "__main__":
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main()
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